A plethora of varied recommendation systems exist on the Internet. Recommendation systems typically apply knowledge discovery techniques to the problem of making product recommendations during a customer interaction. These systems have found great utility in E-commerce, but the current systems are challenged by the exponential growth in the number of customers, and products available to those customers.
Current recommender systems are challenged by their inherent approach to gathering and managing input to generate recommendations. First, they rely on historical user data to develop an initial knowledge base for the recommendation engine. This reliance causes a “cold start” problem, wherein a recommender system is generally inoperable and unreliable until a certain critical mass of user data input has been accumulated by the system. Second, when dealing with sparse input data, these systems are less able to provide relevant recommendations to customers. For example, input data is considered sparse when product lines or brands are emerging rather than mature.
Collaborative filtering is a type of recommender system technology that works by matching input of a customer's preferences to the aggregate inputted or observed preferences of other customers. Collaborative filtering performance degrades as the number of customers or products increases. A recommendation system capable of quickly producing relevant recommendations without relying on management of inputs associated with preference matching would be desirable. It is further desirable to provide such as system capable of handling very large scale application would likewise be desirable.
Recommendation systems have generally evolved in the extremely interactive environment of the World Wide Web, the system of interlinked hypertext documents accessed via the Internet. These systems apply data analysis techniques to help customers find which products they would like to purchase at E-Commerce sites. For instance, a recommender system of AMAZON.COM (www.amazon.com) suggests additional books for purchase by a customer based on books the customer have already purchased from AMAZON, or, based on books a customer has told AMAZON they like. Another recommender system on CDNOW (http://www.cdnow.com) helps customers choose CDs to purchase as gifts, based on other CDs the recipient has liked in the past.
In general, many recommender systems are an application of a particular type of Knowledge Discovery in Databases (KDD) (Fayyad et al. 1996) technique. KDD systems use subtle data analysis techniques to achieve two primary unsubtle goals. First, these systems attempt to save money by discovering the potential for efficiencies. Second, these systems attempt to generate more revenue by discovering ways to sell more products to customers. For instance, companies use KDD to discover which products sell well at which times of year, so they can manage their retail store inventory more efficiently, potentially saving millions of dollars a year (Brachman et al. 1996). Other companies use KDD to discover which customers will be most interested in a special offer, reducing the costs of direct mail or outbound telephone campaigns by hundreds of thousands of dollars a year (Bhattacharyya 1998, Ling et al. 1998). Companies use KDD to discover a new sales model, and then, apply that model to a new sales application. Businesses use KDD to increase sales of existing products by matching customers to the products they will be most likely to purchase.
KDD-based recommender systems are limited in their ability to perform interactively due to their necessary reliance on association of historical data input. For example, while a customer is at specific web site, typically an e-commerce site, the recommender system must learn from the customer's behavior, develop a model of that behavior, and apply that model to recommend products to the customer. The recommendations are based upon the management of historical data input gleaned from other users.
Both collaborative and content-based filtering recommendation systems require management of a base input user profile, driven by textual input by the user, or, selection of various options. This initial input is also known as seed data. The user profile is used to predict relevant items for each user. Initial user inputs can be refined through subsequent user feedback including ranking or rating items, user purchase behavior, and user social network activity. The recommendation system then compares all the collected data and calculates a list of relevant items for the user.
Additionally, current recommender systems typically require iterative interaction by a user, supplemented by historical information concerning the behavior of other users. For instance, in collaborative filtering approaches, like EBAY or AMAZON, a user's past or historical behavior is analyzed for similarities to the behavior of other users. These types of systems are not flexible and do not allow users to actively participate in the development of their personal preference profile. Users cannot remove actions from their history nor can they create an entirely new profile based on desired actions. A user cannot hypothetically add purchases or browsing history to his account that did not actually occur. Additionally, these systems gradually account for activity over some period of time; inputs cannot be changed instantaneously to adjust the personal preference profile.
In content-based filtering approaches, like PANDORA and NETFLIX, the systems require the user to rate items to provide initial seed data. Inherently, these systems do not yield consistent results when their databases have only a few values, creating an inability to derive the most relevant searchable key attributes. Furthermore, where a recommender system requires a minimum amount of seed data to initiate, a user must spend more time at the outset to implement the system to provide relevant results for the user. In addition, content-based filtering approaches are limited to one-to-one comparisons of content types. PANDORA, for example, can only recommend music; similarly, NETFLIX can only recommend films.
It would be preferable to allow each user to understand how the user's inputs are managed to create relevant recommendations. Current recommendation systems do not lend themselves to user transparency in dealing with input management. A user generally does not understand how a particular recommender system manages the user's inputs to generate subsequent product recommendations. Accordingly, a user would not have a sense as to how to influence those recommendations. Input management for current recommender systems does not leverage visual cues or visual elements to assist a user in developing an understanding of how recommendations are developed by the system.
Consequently, in light of the aforementioned limitations, a need exists for methods and systems to manage input for recommendation systems, using visual cues and elements, wherein user input can be changed instantly and resulting recommendations are likewise changed instantly. In addition, a need exists for methods and systems having transparency in operation so a user can play an active role in determining whether the resultant recommendations are consistent with the user's own perception of his or her personal preferences.